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With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

The increase in e-learning platforms, especially Massive Open Online Courses (MOOCs), highlights the necessity for sophisticated, privacy-conscious recommendation algorithms that adjust to evolving learner interactions in IoT-integrated settings. This study introduces an innovative architecture that utilizes Federated Learning (FL) to safeguard user privacy during distributed training on educational platforms. This approach utilizes Graph Convolutional Networks (GCN) to depict intricate user-course interactions as a graph, adeptly capturing higher-order relational dependencies. Furthermore, DistilBERT-based feature extraction generates concise, semantically dense representations from course descriptions, hence improving content relevancy. Real-time IoT data, including user engagement metrics from smart devices, dynamically influences graph connections, facilitating context-aware recommendations.The suggested solution emphasizes scalability and privacy, tackling essential issues in contemporary e-learning environments. Thorough assessments indicate that our methodology substantially surpasses baseline methodologies across various performance indicators, providing exceptionally tailored course recommendations. This research promotes the advancement of adaptive, safe, and efficient recommendation systems for IoT-integrated e-learning, enhancing engaging and personalized learning experiences for users globally.

Over the past decade, the student course recommendation process with secure certificate issuance has remained a critical research area due to the rise of e-learning and personalized learning. The recommendation system enhances the recommended educational resources to improve the students’ learning process. The previous conventional research works shared hybrid content and collaborative filtering techniques, which boosted academic performance, personalized learning, and secure certification for students. However, the existing techniques faced several difficulties in handling the syllabus updates based on evolving recommendations, complexity, and security issues related to certificate issuance. To address the challenges in the existing techniques, the research introduces the Deep Certifier-DX509 model for secure certificate issuance and student course recommendation. The proposed approach exploits the Modified Attention-Enabled Deep Long Short-Term Memory (MA-DLSTM) Model as a recommendation system to suggest the most suitable courses based on users’ prior academic performance, and integrates X509 as the Certificate generation algorithm. Specifically, the incorporation of the X509 Blockchain with Proof-of-Work (PoW) in the certificate sub-system serves as a major contribution to enhance the security with Two-step authentication and generates accurate course recommendations. Experimental results demonstrate that the proposed Deep Certifier-DX509 model shows superior performance, achieving a high Genuine User Rate (GUR) of 0.73, Memory Usage of 453.81KB, Transaction time of 1.03 s, Responsiveness of 2.39s and Throughput of 119.52bps, outperforming the other existing techniques.

Explainable recommendation is important but not yet explored in Massive Open Online Courses (MOOCs). Recently, knowledge graph (KG) has achieved great success in explainable recommendations. However, the e-learning scenario has some unique constraints, such as learners’ knowledge structure and course prerequisite requirements, leading the existing KG-based recommendation methods to work poorly in MOOCs. To address these issues, we propose a novel explainable recommendation model, namely K nowledge-aware R easoning with self-supervised R einforcement L earning (KRRL). Specifically, to enhance the semantic representation and relation in the KG, a multi-level representation learning method enriches the perceptual information of semantic interactions. Afterward, a self-supervised reinforcement learning method effectively guides the path reasoning over the KG, to match the unique constraints in the e-learning scenario. We evaluate the KRRL model on two real-world MOOCs datasets. The experimental results show that KRRL evidently outperforms state-of-the-art baselines in terms of the recommendation accuracy and explainability.

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.

Aiming at the student-centered online music learning platform, this study proposes a Course Recommendation Model for Student Learning Interest Evolution (CRM-SLIE) to improve the accuracy and adaptability of the platform’s course recommendation. This model combines attention mechanism and Gated Recurrent Unit (GRU), and introduces project crossing module, which can effectively capture students’ interest changes and second-order characteristic interaction among courses. The experimental results show that the CRM-SLIE model has excellent performance under different embedding dimensions and the length of student behavior sequence. Especially when the embedding dimension is 64, the Area Under the Curve (AUC) of the model is the highest, and the performance tends to be stable when the sequence length is 20, which is 0.872. Further recall experiments show that with the increase of the number of recommendations, the highest recall rate of CRM-SLIE is 0.364, which is better than other comparative models and can better meet the learning needs of students. In addition, the results of ablation experiments show that the position coding and the way of item crossing have a significant impact on the model performance, and the combination of inner product and Hadamard product is particularly effective in capturing the complex relationship among courses. The research shows that CRM-SLIE model has strong adaptability, robustness and practical application value in the course recommendation task, and can provide personalized and accurate learning resource recommendation for online music learning platform.

This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

The objective of this research is based on human-centered AI in education to develop a personalized hybrid course recommendation system (PHCRS) to assist students with course selection decisions from different departments. The system integrates three recommendation methods, item-based, user-based and content-based filtering, and then optimizes the weights of the parameters by using a genetic algorithm to enhance the prediction accuracy. First, we collect the course syllabi and tag each course from twelve departments for the academic years of 2015 to 2020. Next, we use the course tags, student course selection records and grades to train the recommendation model. To evaluate the prediction accuracy, we conduct an experiment on 1490 different courses selected by 5662 students from the twelve departments and then use the root-mean-squared error and the normalized discounted cumulative gain. The results show that the influence of item-based filtering on the course recommendation results is higher than that of user- and content-based filtering, and the genetic algorithm can find the optimal solution and the corresponding parameter settings. We also invite 61 undergraduate students to test our system, complete a questionnaire and provide their grades. Overall, 83.60% of students are more interested in courses at the top of the recommendation lists. The students are more autonomously motivated rather than holding extrinsic informational motivation across the hybrid recommendation method. Finally, we conclude that PHCRS can be applied to all students by tuning the optimal weights for each course selection factor for each department, providing the best course combinations for students' reference.

With the in-depth development of information technology, various emerging technologies have injected new vitality into traditional education. This article centers on the research of sharing and utilization of Internet-based curriculum resources in better higher mathematics teaching. In order to effectively assemble various higher mathematics teaching course resources, the article designs a higher mathematics teaching resource base. Then the article proposes a DKVMN-FMF model to track and predict the change of students’ knowledge status, based on which it proposes a learning course recommendation model, so as to realize the personalized sharing and utilization of higher mathematics teaching course resources. The study concludes that after using the method proposed in this paper for teaching higher mathematics, the overall mastery level of students on the knowledge points k1 (operation of fractions) and k5 (preliminary knowledge of statistics) is higher, and the number of excellent and good is higher, which can be concluded that the use of the method proposed in this paper in teaching these two knowledge points has a good effect.

Course recommendations are used to help students with different needs to choose courses. However, students’ needs are not always determined by their personal interests, they are also influenced by different curriculum settings, different teacher teams and other factors. Current course recommendation methods lack the consideration of complex relational semantic information that affects students’ needs, resulting in unsatisfied recommendation. To address this issue, we propose Meta-Relationship Course Recommendation (MRCRec) to enrich the expression of relational information. Focusing on complex semantic information of multi-entity relationship and entity association, we construct creatively the multi-entity relational self-symmetric meta-path (MSMP) and associative relational self-symmetric meta-graph (ASMG), which are referred as meta-relationship (MR). We also design an algorithm of meta-relationship correlation measure (MRCor) to obtain semantic correlational information. Then, we adopt the graph embedding to mine and fuse the latent representations of users and that of courses as user preference and course characteristic, respectively. Finally, we optimize matrix factorization to complete recommended task. Comprehensive experiments are conducted on the MOOCCube dataset and XuetangX dataset. The results show that MRCRec can effectively recommend courses for users.